This article examines the integration of object detection algorithms into the navigation systems of unmanned aerial vehicles (UAVs). The primary objective of this integration is to enhance the autonomy and efficiency of UAV flight. The paper provides a comprehensive overview of the fundamental principles of navigation systems, including GPS, INS, and SLAM. It also discusses the utilization of state-of-the-art computer vision algorithms for object detection and classification, such as YOLO, SSD, and Faster R-CNN. The integration methodology is described, including the interaction between the navigation and image processing modules. The architecture of a solution combining the strengths of the considered technologies is also presented. The identified advantages and limitations of power consumption and processing delays confirm the need for further technological improvement to optimise the system’s performance in various scenarios. The potential for scaling and adapting this integrated system for multiple applications, including agriculture, infrastructure monitoring, and search-and-rescue operations, is also explored.